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26 results for “network modularity”
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables
<p>This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes.</p> <p>The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a></p> <p>Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337</p> <p>Details about some of the Supplementary tables:</p> <p>TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used).</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4)</p> <p>Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE.<br> TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion<br> TableS6b_max_PFE_GRN.xt - max PFE<br> TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments. </p> <p>Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1).</p> <p>TableS8a_max_AUFE_SBM.csv<br> TableS8b_max_PFE_SBM.csv<br> TableS8c_middle_AUFE_PFE_SBM.csv</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters.</p> <p>TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426)</p> <p>TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts</p> <p>TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).</p> <p>TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts</p> <p>All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated </p> <p>with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details. </p> <p><strong>femliu1: </strong></p> <p><em>fem-3(q20)ts.; dhs-3p::dhs-3::gfp</em></p> <p><strong>femsod3:</strong></p> <p><em>fem-3(q20)ts.; sod-3p::gfp</em></p> <p><strong>glp1lgg1:</strong></p> <p><em>glp-1(e2144); lgg-1p:lgg-1:gfp</em></p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
Mechanistic interactions as the origin of modularity in biological networks
<p>Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements - such as the species in an ecosystem - as finite-state machines (FSMs) and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity likely emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.</p>
Inductive biases of neural network modularity in spatial navigation
<p>The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. The behavior of agents with this modular architecture also resembles macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brain's modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.</p>
Cactus height increases the modularity of a plant-frugivore network in the Caatinga dry forest
<p class="MsoNormal"><span>Cacti fruits are key resources to many frugivorous animals in Neotropical arid and semiarid regions. However, most studies have focused on a particular animal group or cacti species, but few have explored the overall interactions of such species at the community level. Here we monitored frugivory on five cacti species using camera traps that sampled diurnal and nocturnal interactions. We investigated the structure of interactions with bird, mammal, and reptile frugivores in the Brazilian Caatinga dry forest. We hypothesized that the height of cacti limit interactions with different types of frugivores, which would result in highly structured and modular interaction networks. In 2,929 camera-days, we recorded 23 vertebrate species feeding on cacti fruits, including seven new records, all determined to be primary seed dispersers. As predicted, the cacti-frugivore network was modular and non-nested, with the two shortest cacti species grouped in a module dominated by interactions with reptiles and non-flying mammals. The tallest cacti species were dominated by frugivory interactions with birds and had comparatively less interaction diversity than shorter cacti species. Our results support the contention that cacti are keystone species in semiarid ecosystems where they produce small-seeded fleshy fruits year-round.</span></p>
Dataset: Modular Impedance Matrix Method for Transient Modeling in Pipe Network Systems
<p>Unsteady flow is an important engineering problem in urban pipe network systems, requiring pressure and flow rate management analyses and reliable drinking water quality maintenance. Efficiently solving the hyperbolic partial differential equation and integrating it with various boundary conditions under the complex layout scenarios of pipe networks is a challenging issue for pipeline modelers. Frequency-domain modeling with a time-domain response was developed as an alternative to the traditional method of characteristics. However, this solution requires a substantial array size for large pipe network systems, significantly affecting applicability in field pipe network systems. This study proposes an innovative transient analysis method, the modular impedance matrix method, to solve the most labor- and cost-intensive computational issues affecting the unsteady flow analysis of large, complicated pipe networks. This method was applied to a field pipe network system and its performance compared to existing approaches. The algorithm of the proposed method fundamentally solved the computational problems associated with other methods, and its modular scheme allowed feasible integration with an analytical formulation that can be tailored to the modeler's preferences. The modular impedance matrix method's strength can be amplified according to the size and complexity of the pipe network system owing to its unique complementary validation capability. </p>
Data from: The role of social attraction and social avoidance in shaping modular networks
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Inductive biases of neural network modularity in spatial navigation
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Cactus height increases the modularity of a plant-frugivore network in the Caatinga dry forest
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Mechanistic interactions as the origin of modularity in biological networks
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Data from: Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree-herbivore networks.
Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint.
Dataset (MATLAB format) from Chen Kang et al (2021) Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.
<p><strong>Summary</strong></p> <p>These experiments simultaneously measure neuronal responses from the two hemispheres of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. We inactivate activity of one hemisphere (left or right) in some trials and both hemispheres in others. Data from 135 recording sessions are included in this release.</p> <p>This dataset contains data from 39 mice (age > P60). 9 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) and 30 PV-IRES-Cre (JAX Stock#008069) crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846), were used for simultaneous electrophysiology and photoinhibition. The experiments (including experiment methods) are described in the following publication:</p> <p><em>Chen, G., Kang, B., Lindsey, J., Druckmann, S., Li, N. (2021). Modularity and robustness of frontal cortical networks. Cell 184, 1-14. doi: 10.1016/j.cell.2021.05.026</em></p> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Chen G, Li N (2021); Data and simulations related to: Modularity and robustness of frontal cortical networks. Chen et al (2021) Cell, 184(14):3717-3730.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6713616">http://dx.doi.org/10.5281/zenodo.6713616</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>analysis_scripts</strong>”</p> <p>2) unzip and combine all "<strong>data_structure_*.mat</strong>" and "<strong>meta_data_*.mat</strong>" files into a single folder “<strong>datafiles</strong>”.</p> <p> • <strong>data_structure_*.mat </strong>- contains raw spike data for one session.</p> <p> • <strong>meta_data_*.mat</strong> - contains the meta data information for one session.</p> <p> There is one "<strong>meta_data_*.mat</strong>" file for each "<strong>data_structure_*.mat</strong>"</p> <p>3) Run scripts within "<strong>analysis_scripts</strong>". Follow the instruction in the "<strong>code instruction</strong>".</p> <p> </p> <p><strong>Data analysis</strong></p> <p>The extracellular recording traces were band-pass filtered (300-6 kHz). Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to spike sorting to extract single units. Spike sorting was either manual (Guo et al., 2014b) or using Kilosort2 (https://www.github.com/MouseLand/Kilosort2) (Pachitariu et al., 2016) followed by manually curated with the Phy 2.0 beta 1 GUI (https://github.com/cortex-lab/phy) (Rossant et al., 2016) and manual inspection (Guo et al., 2014a). Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width < 0.35 ms were defined as fast-spiking (FS) neurons and units with spike widths > 0.45 ms as putative pyramidal neurons. Units with intermediate values (0.35 - 0.45 ms) were excluded. We concentrated our analyses on the putative pyramidal neurons.</p>
Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures
<p>This is the training data for the paper "Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures"</p>
Data from: Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree-herbivore networks.
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Unconsciousness reconfigures modular brain network dynamics
<p>Time-dependent adjacency matrices per state of consciousness</p>
Data from: The relative efficiency of modular and non-modular networks of different size
Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: 'small' and 'large', and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.
Cheating interactions favor modularity in mutualistic networks
<p>A fundamental fact about mutualisms is that these mutually beneficial interactions often harbor cheaters that benefit from the use of resources and services without providing any positive feedback to the other players. The role of cheaters on the evolutionary dynamics of mutualisms has long been recognized, yet their broader consequences to the community level, and beyond species they interact with, is still poorly understood. Because mutualisms form networks that often involve dozens to hundreds of species, indirect effects generated by cheaters may cascade through the whole community, reshaping trait evolution. Here, we study how harboring cheating interactions can influence coevolution in mutualistic networks. We combine a coevolutionary model, empirical data on animal-plant mutualistic networks, and numerical simulations and show that a higher frequency of cheating interactions in a network can lead to the formation of groups of species phenotypically similar to each other and distinct from other groups, generating higher trait disparity. The resulting clustered trait patterns, in turn, change the patterns of interaction in simulated networks, fostering the formation of modules of interacting species. Our results indicate that cheaters contribute to generate phenotypic clusters in mutualistic networks, counteracting selection for convergence imposed by mutualists, and favoring the emergence of modules of interacting species. Based on these results, we suggest that cheaters might be a fundamental element for our understanding of the evolution of mutualistic networks.</p>
Data from: Low modularity and specialization in a commensalistic epiphyte–phorophyte network in a tropical cloud forest
Species interactions can shape the structure of natural communities. Such sets of interactions have been described as complex ecological networks, an example of which is the commensal network formed by epiphyte–phorophyte interactions. Vascular epiphytes germinate and grow on phorophytes (support trees), assuming a horizontal distribution (among the phorophyte species) and a vertical distribution (from the base of the tree trunk to the crown of phorophytes, i.e., through ecological zones). Here we investigated the organization of these structural dimensions of the epiphyte–phorophyte network in a Brazilian tropical montane cloud forest. The analyzed network, comprising 66 epiphyte species and 22 phorophyte species, exhibited a nested structure with a low degree of specialization, a typical pattern for epiphyte–phorophyte networks in forests. The network was slightly modular, with 65% of the species common to three modules, and had vertical structure corresponding to the vertical organization of the phorophytes. The size (diameter at breast height) of phorophyte individuals influenced the network structure, possibly due to the increase in habitat area, the time available for colonization by epiphytes, and a greater number of microenvironments. We found that the distribution of the epiphyte species differed between the phorophyte ecological zones, with greater richness in the lower portions and greater abundance in the upper portions of the phorophytes. The results provide relevant guidance for future research on the characteristics and the vertical and horizontal organization of vascular epiphyte and phorophyte networks.
Data from: Low modularity and specialization in a commensalistic epiphyte–phorophyte network in a tropical cloud forest
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.